Policy analysis and action decision tool

ABSTRACT

Using stochastic directed graphs, a social network stochastic directed graph model allows for policy analysis and action. An activity generator may be used for creating agents that represent a population stratum. Agents may be proportionally selected to the size of the population stratum and representative activities that are associated with said population stratum. Agents have one or more conditional probabilities attached to the activities, which indicate the likelihood of interaction between agents and one or more agents or actors. Outcomes for the interactions may be accumulated. Based on these outcomes, which include benign and acute, a multinomial probability distribution may be estimated.

GOVERNMENT LICENSE RIGHTS TO CONTRACTOR-OWNED INVENTIONS MADE UNDERFEDERALLY SPONSORED RESEARCH AND DEVELOPMENT

This invention was made with government support under Grant No.IF32AA015876-01A1 awarded by the National Institute on Alcohol Abuse andAlcoholism of the National Institutes of Health. The government hascertain rights in the invention.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of provisional patentapplication Ser. No. 60/776,680 to Said et al., filed on Feb. 27, 2006,entitled “Graph-Theoretic Policy Decision Tool,” which is herebyincorporated by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an example of a flow diagram for creating a social networkstochastic directed graph model based on a stochastic directed graph.

FIG. 2 shows another example of a flow diagram for creating a socialnetwork stochastic directed graph model based on a stochastic directedgraph.

FIG. 3 shows another example of a flow diagram for creating a socialnetwork stochastic directed graph model based on a stochastic directedgraph.

FIG. 4 shows an example of a block diagram of a social networkstochastic directed graph system.

FIG. 5 shows another example of a block diagram of a social networkstochastic directed graph system.

FIG. 6 shows an example of a block diagram of an apparatus.

FIG. 7 shows another example of a block diagram of an apparatus.

FIG. 8 shows an example of a time of day stochastic directed graph withacute outcomes.

FIG. 9 shows an example of a social network for an alcohol user.

FIG. 10 shows an example of an adjacency matrix summarizing connectivitystrengths in the alcohol users directed graph.

FIG. 11 shows a simple digraph with four vertices and four directededges.

FIG. 12 shows an example of an abbreviated alcohol tree.

FIG. 13 shows zip codes and percentages within Fairfax County, Va.

FIG. 14 shows a continuation of zip codes and percentages within FairfaxCounty, Va.

FIG. 15 shows zip code population and demographic information forFairfax County, Va.

FIG. 16 shows a continuation of zip code population and demographicinformation for Fairfax County, Va.

FIG. 17 shows the alcohol seller (Virginia's ABC stores), gallons ofalcohol sold and gross sale figures in dollars.

FIG. 18 shows alcohol establishment license information and status forFairfax County, Va.

FIG. 19 shows a continuation of alcohol establishment licenseinformation and status for Fairfax County, Va.

FIG. 20 shows a low amount of alcohol availability outlets in FairfaxCounty, Va.

FIG. 21 shows a medium amount of alcohol availability outlets in FairfaxCounty, Va.

FIG. 22 shows a high amount of alcohol availability outlets in FairfaxCounty, Va.

FIG. 23 shows leading causes of death in Va.

FIG. 24 shows a continuation of leading causes of death in VA.

FIG. 25 shows a continuation of leading causes of death in VA.

FIG. 26 shows a continuation of leading causes of death in VA, withFairfax County noted.

FIG. 27 shows resident alcohol induced deaths by race and sex asunderlying causes of death in the year 2000 in VA.

FIG. 28 shows resident alcohol induced deaths by zip code and race/sexin the year 2000 in Fairfax County, Va.

FIG. 29 shows a sample of motor vehicle crashes.

FIG. 30 shows a continuation of motor vehicle crashes.

FIG. 31 shows a sample of crime statistics.

FIG. 32 shows a continuation of the sample of crime statistics.

FIG. 33 shows a continuation of the sample of crime statistics.

FIG. 34 shows a continuation of the sample of crime statistics.

FIG. 35 shows a continuation of the sample of crime statistics.

FIG. 36 shows a continuation of the sample of crime statistics.

FIG. 37 shows a continuation of the sample of crime statistics.

FIG. 38 shows another example of an alcohol tree directed graph.

FIG. 39 shows examples of conditional probability of being an alcoholmisuser given ethnicity, job class and zip code in Fairfax County, Va.

FIG. 40 shows a continuation of examples of conditional probability ofbeing an alcohol misuser given ethnicity, job class and zip code inFairfax County, Va.

FIG. 41 shows a continuation of examples of conditional probability ofbeing an alcohol misuser given ethnicity, job class and zip code inFairfax County, Va.

FIG. 42 shows a continuation of examples of conditional probability ofbeing an alcohol misuser given ethnicity, job class and zip code inFairfax County, Va.

FIG. 43 shows a continuation of examples of conditional probability ofbeing an alcohol misuser given ethnicity, job class and zip code inFairfax County, Va.

FIG. 44 shows an example of JAVA class nodes and their relationship.

FIG. 45 shows an example of Fairfax County, Va. with the intensity andrepresentative scale of acute outcomes with probabilities based onactual data.

FIG. 46 shows an example of Fairfax County, Va. with a rerun simulationwith modifications showing only low outlet availability in each zipcode.

FIG. 47 shows an example of detailed output from the alcohol treesimulator.

FIG. 48 shows a continuation of an example of detailed output from thealcohol tree simulator.

FIG. 49 shows a continuation of an example of detailed output from thealcohol tree simulator.

FIG. 50 shows a continuation of an example of detailed output from thealcohol tree simulator.

FIG. 51 shows VA state-owned ABC stores located in Fairfax County, Va.

FIG. 52 shows examples of alcohol establishments, both off and onpremises, that are licensed to sell alcohol.

DETAILED DESCRIPTION OF THE INVENTION

The claimed invention relates to an updatable social network stochasticdirected graph model that may be embodied as systems, methods and/orcomputer program products (e.g., software, interactive webpages, etc.).In particular, the social network stochastic directed graph model mayutilize one or more stochastic directed graphs to help formulate publicpolicy.

I. INTRODUCTION

Alcohol is legally sold in the United States without a prescription tothose who are twenty-one years and older. Also referred to as ethanol orethyl alcohol, alcohol can create a euphoric sense in low amounts thatoften seduces the user (e.g., consumer) into consuming higher dosages.At higher dosage levels, alcohol is a depressant that suppresses bothcognitive and/or motor functions in the brain. With respect to cognitivefunctions, a suppression result is impairment of judgment. With respectto motor functions, a suppression result is weakened ability to react tostimuli. Both may lead to a range of acute alcohol-related outcomes(also referred to herein as acute outcomes). An acute outcome is definedas any decision or action taken by a user, or one or more consequencesresulting from the decisions or actions of a user. Nonlimiting examplesof acute outcomes include assault, suicide, sexual assault, murder,domestic violence, child abuse, automobile accidents caused by drivingunder the influence (DUI) or driving while intoxicated (DWI) (includingfatal accidents either from DUI or DWI), alcohol induced sexuallytransmittal diseases, etc. At extremely high dosages, alcohol may evenlead to death. According to the 2002 American Heritage Stedman's MedicalDictionary, lethal dose 50 (LD50) for alcohol occurs at 0.35 BloodAlcohol Concentration (BAC).

It is well known that LD50 is that dosage of a substance that causes ½the exposed population to die. Such dosage can be for any medication,such as chemotherapy drugs, a toxic poison (e.g., ricin, etc.), drugs(e.g., alcohol, heroin, etc.), everyday substances (e.g., drain cleaner,etc.). LD30, by analogy, would cause 30% of the exposed population todie.

Interventions that mitigate the undesirable acute outcomes need to beexplored. However, they are often based on an incomplete understandingto the alcohol usage. In general, the study of alcohol usage and itseffects can be addressed at different scales. The broadest understandingcomes from studying the societal dynamics surrounding alcohol use.

The alcohol system of the present invention typically involves complextemporal and spatial interactions among a multitude of people. Theseinclude users (such as causal drinkers, heavy users/alcohol abusers,binge drinkers, under aged or young drinkers, alcoholics, etc.), familymembers and peers' of users, non-users, alcohol producers anddistributors, law enforcement, courts, prevention activities andtreatment centers. Additionally, the alcohol system may be understood interms of sub-populations and geo-spatial interactions among diversecommunities. Analogously, understanding the alcohol system involvesmany, if not most or all, of the same or similar issues, as well as thesame or equivalent level, of complexity that ecologists face inunderstanding conventional or classical ecological systems. Classicalecological systems may be defined as a collection of multiple organisms,their environment, their relationships and their interactions.

Because the consequences of alcohol misuse tend to be severe, includingviolence and/or life threatening situations, for individuals andsociety, a tool that provides policy insights into the effectiveness ofinterventions is needed.

II. SOCIAL NETWORK STOCHASTIC DIRECTED GRAPH MODEL

Based on the concept of a stochastic directed graph (also referred toherein as directed graph or digraph), the present invention can be usedas a simulation model to represent any social network order or policy.For instance, the model can apply to sales, distribution and/orconsumption of alcohol. Other nonlimiting examples of social networks inwhich the model may be applied include drugs (e.g., illegal substances,over-the-counter drugs, prescription drugs, etc.), tobacco, banks andsimilar financial institutions, gas stations, food vendors, retailstores, wholesale stores, sporting events, concerts, diseases and/orother medical-related issues, healthcare, homeland security, elections,etc.

Referring to FIGS. 1-7, the present invention may be embodied in theform of a physical or tangible computer-readable medium (e.g., computerprogram product, etc.), system or apparatus.

The tangible computer readable medium may be encoded with instructionsfor creating a social network stochastic directed graph model computerprogram that are executable by an instruction execution system.

Examples of tangible computer readable mediums include, but are notlimited to, a compact disc (cd), digital versatile disc (dvd), usb flashdrive, floppy disk, random access memory (RAM), read-only memory (ROM),erasable programmable read-only memory (EPROM), optical fiber, etc.

The instructions may be written using any computer language or format.Nonlimiting examples of computer languages include Ada, Ajax, C++,Cobol, Java, Python, XML, etc.

The instruction execution system may be any apparatus (such as acomputer or processor) or “other device” that is configured orconfigurable to execute embedded instructions. Examples of “otherdevice” include, but are not limited to, PDA, cd player/drive, dvdplayer/drive, cell phone, etc.

The physical or tangible computer-readable medium may be encoded withinstructions for creating a social network stochastic directed graphmodel. As illustrated in FIGS. 1-3, upon execution of the “socialnetwork stochastic directed graph model,” one or more processors may usean activity generator for creating agents that represent a populationstratum S105; proportionally select agents according to the size of thepopulation stratum and according to representative activities associatedwith the population stratum S110; accumulate outcomes for eachinteraction S115; and estimate a multinomial probability distributionbased on the outcomes S120. An agent may be an organism, person ororganization. Agents may have one or more conditional probabilitiesattached to the activities, which tends to indicate the likelihood of aninteraction between the agents with at least one other agent or actor.Actor is defined as another agent, group of agents or institution. Theoutcomes may include benign outcomes and acute outcomes.

Referring to FIGS. 4-5, as a social network stochastic directed graphsystem 405 using one or more stochastic directed graphs for formulatingpublic policy, the present invention includes an activity generator 410,an agent selector 415, an outcome accumulator 420 and an estimator 425.It may even further include an interaction developer 530.

Referring to FIGS. 6-7, as an apparatus 605 for generating a socialnetwork stochastic directed graph, the present invention may similarlyinclude an activity generator 610, an agent selector 615, an outcomeaccumulator 620 and an estimator 625. It may even further include aninteraction developer 730. The apparatus 605 may be a stand-alone,hand-held and/or portable device.

Activity generator 410, 610 may be configured for creating agents thatrepresent a population stratum. Agent selector 415, 615 may beconfigured for proportionally selecting agents to the size of thepopulation stratum and the representative activities associated with thepopulation stratum. The agents may have one or more conditionalprobabilities attached to the activities. Each conditional probabilitymay indicate the likelihood of interaction(s) between agents and atleast one other agent or actor. Outcome accumulator 420, 620 may beconfigured for accumulating outcomes for each interaction. Outcomes mayinclude benign outcomes and acute outcomes. Estimator 425, 625 may beconfigured for estimating a multinomial probability distribution basedon the outcomes.

Where an interaction developer 530, 730 is included, it may beconfigured for developing interactions among the agents to adjust anyconditional probability that is in connection with acute outcomes.

As another embodiment of the present invention, a hierarchical Bayesianstructure may be introduced to the stochastic directed graph S220. Apurpose of such introduction is to allow one to vary the probabilitydistribution. Moreover, the hierarchical Bayesian structure may increasethe richness of the possible behaviors for agents from a given clusteror class.

As another embodiment of the present invention, vertices of thestochastic directed graph represent the state of the agents. Thisrepresentation entails the current state of the agent, not the agentitself.

As another embodiment of the present invention, edges of the stochasticdirected graph represent at least one decision that takes the agentsfrom one state into another state. This transitioning between states maybe iterative. Hence, multiple states may be involved.

As another embodiment of the present invention, one or more conditionalprobabilities may be attached to the edges. It is possible that thesocial network stochastic directed graph model is asymmetric. Forexample, an agent may be associated with one police officer. However,the same police officer may be related to multiple actors. It is alsopossible that the social network stochastic directed graph model issymmetric. For instance, where one agent is associated with a policeofficer, that same police officer is associated with one agent or actor.

As another embodiment of the present invention, the stochastic directedgraph may be time dependent. Time can either be continuous or discrete.Because the present invention often involves one or more levels/layersof social networks, the probability of interactions may change overtime. Given various factors affecting agents, such as behavioral and/orsocietal influences or catalysts, over a span of time (e.g., day, week,month, year, etc.), such change is likely to occur even if the samesocial network exists at each level or layer. As exemplified in FIG. 8,a time-dependent stochastic directed graph shows a social network andits evolution throughout the time of day and week, as well as acuteoutcomes.

As another embodiment of the present invention, each of the agents maybe a member of a class. The class may be homogenous. Agents may beaggregated into relatively homogenous classes of similarly behavingagents. As an example, agents who have been arrested under DWI chargescan be aggregated into certain prevalent classes of drinking behaviors.

The present invention allows for multiple agents and multiple classes.For example, there may be 30 agents per class, and there may be 15classes. In general, the agent should be unique; the classes should bemutually exclusive. Examples of classes include, but are not limited to,race/ethnicity, income level, education, gender, age, job, etc. Inessence, an agent in a particular class may be a White male, blue collarworker and a misuser. An agent in another class may be a Hispanicfemale, white collar worker and an alcoholic user.

The overall concept is that relatively homogenous clusters or classes ofpeople are identified along with their daily activities. In the parlanceof social networks, agents generally represent people, whetherindividually or as an organization. They may also represent organisms,other living things and can even be nonliving things. The activities arecharacterized by different states in the directed graph. States canrefer to a multitude of factors that describe the status or situation ofan agent. Factors include, but are not limited to, physical location,present activity, behavior, physical or mental conditions, blood alcoholcontent (BAC) level, etc. Decisions resulting from an agent's actionsmove the agent from state to state within the directed graph. The leafnodes in the directed graph represent a variety of outcomes, some ofwhich are benign and some of which are acute alcohol-related outcomes. Abenign outcome is defined as an outcome that is not an acute outcome.The agents have probabilities associated with their transit from stateto state through the directed graph. As agents are introduced into thedirected graph model, their outcomes, whether benign or acute,accumulate so that a multinomial probability distribution can beestimated. An ultimate goal is to create a tool that will be useful forpublic policy formulation by allowing an analyst to investigatepotential effects of interventions.

A two-part strategy may be implemented. The first part of the strategyinvolves the technical level. As another embodiment of the presentinvention, the conditional probability is adjustable. By allowing one tochange conditional probabilities, the structure of the directed graphmay reveal how those adjustments affect the probability distributionover outcomes. In other words, one can see how agents and/or actorsinteract. It is possible that an intervention may reduce the incidenceof one acute outcome, but increase the incidence of other acuteoutcome(s). For example, increasing police patrols at off-license areasselling alcohol may reduce assaults against sellers. However, as aconsequence, the occurrence of DWIs and domestic violence may increasebecause users making alcoholic purchases at these off-license locationsoften consume the purchased alcohol elsewhere.

The second part of the strategy, as another embodiment of the presentinvention, involves developing interactions among agents to adjust anyconditional probability that is in connection with one or more acuteoutcomes. Developing interactions may be achieved using an interactiondeveloper S325. This aspect can generally be referred to the basis forthe policy tool. The ability to adjust the probabilities can allow oneto see the intervention(s) and different combinations of interventionsand to determine or achieve probability adjustments leading to afavorable reduction in the probabilities associated with acute outcomes.For instance, focusing on reductions of drinking behaviors withincertain populations or certain geographic regions may reduce the overallprobability of acute outcomes. In essence, the policy system as a wholein order to evaluate the best interventions for reducing overallincidences of acute outcomes. As a policy tool, this study will behelpful because current non-systematic approaches tend to yield verylimited capabilities.

As another embodiment of the present invention, the structure of thestochastic directed graph and the conditional probability is based oncollected data. Collected data includes, but is not limited to, surveyeddata, census data, administrative record data, national data, statedata, local data, expert opinion and any combination thereof.

As another embodiment, the present invention may also be applied to anygeographical area. For example, it may apply to a specific city or town(e.g., New York, N.Y., Los Angeles, Calif., Washington, D.C., Chicago,Ill., Miami, Fla., etc.), county (e.g., Fairfax County, Va., DallasCounty, Tex., Wayne County, Mich., San Francisco County, WyandotteCounty, Kans., etc.), state (e.g., MO, IA, OH, ID, NH, etc.), or eventhe entire nation.

The example described below throughout indicates how the presentinvention may involve agents from diverse communities that representinga comprehensive view of an alcohol system. FIG. 9 shows an example of asocial network for an alcohol user. This same network in this figure mayalso apply to non-alcohol users with some edges removed.

Furthermore, the alcohol example described below may also offer adynamic, simulation view of the system with simultaneous assessment of avariety of acute outcomes. FIG. 10 shows an example of an adjacencymatrix summarizing strengths of connectivity in the alcohol usersdigraph. The x's are place holders for numerical probability values. Inthis figure, there is a general symmetry, but the bidirectionalprobabilities between any two nodes are likely to be unequal.

This approach may incorporate not only factors, such as geographicdistribution and operating hours of alcohol outlets (e.g., bars, stateliquor stores, grocery and convenience stores that sell beer and wine,restaurants, etc.), but also mobility information. Interventionprocedures can be assessed by altering group-specific probabilitystructures. Nonlimiting examples of intervention procedures includechanges in alcohol service and distribution policies, law enforcementprocedures, judicial sentencing policies, taxing policies and othergovernment legislation. As an aspect, the present invention allows forsimultaneous assessment of the effect of interventions on theprobability distributions of acute outcomes with a dynamic simulationmodel.

The selected target experimental site for the example is Fairfax Countyof Northern Virginia. There are several reasons for such a selection.There are readily identifiable subpopulations within Fairfax County,including subpopulations that exhibit problem drinking behaviors. Theseinclude, inter alia, university and high school age populations,military populations, white-collar and blue-collar workers, andsignificant immigrant communities. In addition, there is significantlocal expertise in alcohol studies and access to data on alcohol use inthis geographic region from public records and from alcohol relatedsurveys. For instance, local experts may have access to, and experiencewith, the Virginia Department of Alcoholic Beverage Control, theVirginia Alcohol Safety Action Program, and other remediation andtreatment programs.

It should be noted that in no way does this present invention limit thepolicy tool to the above selected alcohol system or target experimentalsite. Rather, using Fairfax County of Northern Virginia as an exampleshows how the policy tool can be implemented. It is within the scope ofthe present invention that the policy tool be implementable in allgeographical capacities, whether or not such geographical capacitieshave less, more or an equivalent amount of collectable data.

III. STOCHASTIC DIRECTED GRAPHS

A directed graph G is a pair (V, E) where V is a set of elements calledvertices or nodes, and E is a subset of the set of all ordered pairs (a,b), where a and b are vertices. An element of E is called an edge or anarc of G. The pair (a, b) is not the same as the pair (b, a). Typically,the direction of the edge (a, b) may be regarded as flowing from a to b.Conventionally, an edge of the digraph can be represented as two pointsrepresenting a and b with an arrow whose tail is at a and whose head isat b. A graphic example with four vertices labeled a, b, c, d and fouredges can be seen in FIG. 11.

As an embodiment, the alcohol system may be modeled with anagent-dependent, time dependent stochastic digraph. The vertices of thedigraph represent the state of an agent (including factors such asphysical location, present activity, BAC level, etc.). The edgesrepresent a decision/action that takes the agent into a new state. Theagent generally represents any individual in the population, includingalcohol users and non-alcohol users (also referred to as non-users). Theedge going from one state to another generally has a conditionalprobability attached to it; hence the notion of a stochastic digraphexists. The conditional probability attached to a given edge depends onthe specific sub-population, from which the agent is drawn; hence thepresent invention is agent-dependent. The conditional probability mayalso depend on the time of day or night; hence the present invention isalso time-dependent.

The exemplified model herein focuses on short-term modeling of a singleday. In essence, this model tends to remove the need to model courtaction, but not law enforcement. It also tends to remove the need tomodel the impact of producers, but not distributors. Furthermore, ittends to remove the need to model treatment centers, but not preventionprograms. By limiting the model to one day, the modeling process and thedata requirements can be simplified.

Yet, the present invention also allows for the modeling of moresubtleties, as well as broadened limitations. Such approach providesmore modeling flexibility so that more complex or less complex modelscan be formulated, depending of the purpose(s) and goal(s) of a study.Modeling more complex interactions can introduce a need for puttingfeedback into the system. They can also introduce a need for collectingadditional data for estimating conditional probabilities built into themodel.

Generally, the directed graph is tree-structured with leaf nodes (i.e.,vertices with edges pointed to them, but for which there are no edgespointed away from them). The leaf nodes correspond to the outcomes,which may be one of the benign outcomes or one of the acute outcomes.

In the exemplified model, an agent can experience only one acute outcomeper day. If the agent experiences no acute outcomes in the day, theagent has a benign outcome. A path through the digraph represents thepath of an agent from the agent's initial state to final resolution ofhis or her decisions/actions at the leaf node. The path may be verysimple; it may involve only two or three vertices. However, it may bevery complex, following the many activities of an agent throughout theentire day. Because there may be conditional probabilities attached toeach edge, the outcome for agents with identical starting states may bevery different. Also, there may be feedback loops, where, for example,there exists repeating offenders with old and/or new patterns ofbehavior.

Using a directed graph can prove to be a very fruitful device tostimulate clear thinking about the possible sequence of states and/oractions for any agent. By creating the directed graph, one can sequencethe set of states such that the consequences of a single decision/actioncan be carefully envisioned independent of the agent. The agent mayaffect the conditional probabilities (including possibly setting some tozero), but not the general structure of the digraph. Normally,interventions alter conditional probabilities differentially for agentsfrom different sub-populations, socioeconomic status, geographicregions, age, and racial/ethnic backgrounds. Because the model andsimulation may account for a wide variety of acute and benign outcomessimultaneously, one is able to estimate probabilities of outcomessimultaneously (i.e., estimate probability distributions over the leafnodes).

The present invention contrasts that of the known, more conventional,pure statistically-based alcohol studies, which examine static data anddraw limited conclusions. For example, most alcohol consumption isassociated with beer consumption. Beer purchased in outlets is oftenconsumed nearby. To reduce local violence, more law enforcement may beadded to police these outlets. Alternatively, taxes on alcohol purchasescan be increased. However, there appears no way to assess the impact onother acute outcomes or determine whether such actions reduce theoverall probability of acute outcomes. The agent-based stochasticdigraph model/simulation allows for the dynamic adjustment ofconditional probabilities such as these so that the final distributionof probabilities among all outcomes may be assessed. Quite often, onewould like to raise the probability of a benign outcome whilesimultaneously lower the probability of acute outcomes, such as assault,domestic violence or DWI. However, it is possible that certaininterventions may reduce probabilities associated with some acuteoutcomes, increase the probabilities associated with other acuteoutcomes, and still not reduce the overall probability of acuteoutcomes.

The stochastic digraph model can be exploited as a simulator by using aMonte Carlo simulation to decide a path through the digraph for eachagent generated. At each node (state), there may be a conditionalprobability distribution for the next node (state) associated with theagent and time of day and the decision/action that the agent makes. Thesame decision/action for the same time of day and the same agent canpossibly have different outcomes. However, it must have some outcome sothat the conditional probabilities must add to one. In other words, ateach node a randomly generated number between zero and one may be usedto decide to which node the agent is taken. This randomness mayultimately lead each agent to transition to one leaf node, which may bean acute outcome or a benign outcome. By introducing many agents intothe digraph, one can dynamically simulate the probability distributionof outcomes. By adjusting the interventions, which corresponds toadjusting certain transition probabilities, one can examine how thoseinterventions affect the probability distributions of outcomes.

FIG. 12 illustrates a simplified version of the stochastic directedgraph, namely the alcohol tree. The bottom portion is replicated foreach of the nine ethnicity-job class pairs. In this tree, the subtreelocated under the white race is replicated (but not shown) under theblack race and under the Hispanic ethnicity. Similarly, the subtreelocated under blue collar is also replicated (but not shown) under whitecollar and unemployed. This replicated structure makes this treestructure ideal for programming in an object-oriented language, such asJAVA.

IV. THE DATA

A. At The Macro Level

A broad array of data sources may be necessary to facilitate thedevelopment of an alcohol ecosystem model. Data is required for amultitude of purposes, ranging from providing detailed populationcharacteristics for the areas to be modeled to information on specificdrinking behaviors by age and demographic groups.

Demographic information may be obtained from the U.S. Bureau of Census(Census). Census data provides detailed information on demographicdistributions of characteristics such as age, gender, race/ethnicity,and socioeconomic status (e.g., median income, poverty status, etc.).These data are available from the decennial census and updates. Data onall full-count and long form census items are typically available at thetract and block group levels of geography.

Data on alcohol-related behaviors are critical to the development of thealcohol digraph model because specific inputs are often needed for themodel. Additionally, data on alcohol-related outcomes are typically usedfor model calibration. Local and state databases may provide some of therelevant information. However, it may be the case where no single datasource is able to provide the necessary detailed data. Although nosingle source of data is likely to be sufficient, data may be adequatelyobtained from a combination of local, county, state, national, andspecialty data sources.

These data sources can be supplemented with national databases,including but not limited to those presented and developed by theNational Institute Alcohol Abuse and Alcoholism's (NIAAA) AlcoholEpidemiologic Data System (AEDS). AEDS issues special reports on topicssuch as alcohol problem indicators and alcohol-related mortality trends.A multitude of national data sources may also be utilized, including theCenter for Disease Control's (CDC) Behavioral Risk Factor SurveillanceSystem, National Survey on Drinking and Driving Attitudes and Behaviors,National Longitudinal Alcohol Epidemiologic Survey (NLAES), NationalAlcohol Surveys (conducted by the Alcohol Research Group), NationalHealth Interview Survey (NHIS), National Health and Nutrition Surveys(NHANES), National Survey on Drug Use and Health (NSDUH), and theNational Survey of Substance Abuse Treatment Services (NSSATS). Thiscombination of data sources can provide a varied and rich source ofinformation for model building.

B. At The Micro Level

Focusing on Fairfax County, Northern Virginia was a deliberate choicebecause of advantages previously mentioned. The required demographic andgeographic data are available from county sources. Extensively studied,the Northern Virginia (metropolitan Washington, D.C.) transportation hasan excellent database. Alcohol usage data are available from both theVirginia Department of Alcoholic Beverage Control and the VirginiaAlcohol Safety Action Program. Information on acute outcomes involvingfelonies is part of the record of the Circuit Court (e.g., the VACircuit Court of the 19th Judicial District).

Data can be collected for Fairfax County from a multitude of places.These include, but are not limited to, the Virginia Department of MotorVehicles (DMV), Virginia Department of Alcoholic Beverage Control,Virginia Police Department, Fairfax County Police Department, FairfaxCounty Crime Data Analysis Department, Fairfax County CriminalInvestigation Bureau, Virginia Department of Health, Hospitals, INOVA.Fairfax Hospital, INOVA. Trauma Center, Fairfax/Falls Church CommunityServices Board (CSB), Office of Substance Abuse Services (OSAS), SAMHSA,Virginia Commonwealth Tax Administration Office, Fairfax County Board ofSupervisors, U.S. Postal Address Management Services, Fairfax CountyHealth Information Services, Fairfax/Falls Church Community ServicesBoard Alcohol Drug Services, Division of Alcohol and Drug Services,Virginia Health Statistics, Fairfax County Citizen Assistance andInformation, Fairfax County Demographic Information, Fairfax CountyElectoral Board, Fairfax County Geographic Information Services (GIS),Fairfax County Public Affairs, Fairfax County Maps and PublicationsOffice, and Fairfax County Department of Management and Budget.

The following Fairfax County data may be used for simulation purposes.FIGS. 13-14 show zip codes and population percentages within FairfaxCounty. FIGS. 15-16 show zip code population and demographicinformation. FIG. 17 shows alcohol seller (ABC Store), gallons ofalcohol sold and gross sale figures in dollars. FIGS. 18-19 show alcoholestablishment license information and status. FIG. 20 shows a low amountof alcohol availability outlets. FIG. 21 shows a medium amount ofalcohol availability outlets. FIG. 22 shows a high amount of alcoholavailability outlets. FIGS. 23-26 show leading causes of death. FIG. 27shows resident alcohol induced deaths by race and sex as underlyingcauses of death in VA of 2000. FIG. 28 shows resident alcohol induceddeaths by zip code and race/sex in Fairfax County, Va. of 2000. FIGS.29-30 show samples of motor vehicle crashes. FIGS. 31-37 show samplecrime statistics.

C. Impact on Community-Based Programs

The present invention provides for simultaneous assessment of the impactof interventions on the probabilities of acute outcomes. Such featureallows for a choice of strategies that can reduce societal cost both inhuman terms (e.g., reduction of unnecessary deaths) and in financialterms (e.g., costs society incurs when prosecuting criminal activityrelated to undesirable alcohol behaviors). Furthermore, the use of therelatively homogeneous clusters of agents in the model formulation hasthe added advantage of identifying at-risk subpopulations, and thedynamics of their adverse alcohol-related behaviors.

Moreover, the present invention's dynamic character makes it possible toidentify specific times, places and circumstances for adverse behaviorsfor subpopulations, and thus making subpopulation specificcommunity-based programs possible. Alcohol abusers and alcoholics oftenneed intense treatment therapies, including detoxification, educationalprograms and medical treatment. In contrast, the user who is physicallyless tolerant of alcohol is often more profoundly affected by acuteoutcomes and usually needs a different type of treatment. Identifyingsubpopulations at risk for both types of behaviors can likely reduceoverall societal cost by targeting appropriate treatment.

V. ESTIMATING THE PROBABILITIES

A. The Fairfax County, Va. Model

A general strategy in estimating the probabilities involves using afrequentist approach based on collected data. For the most part, datawas not collected according to a randomized designed experiment. Hence,the relative frequencies may be somewhat problematic.

Using Northern Virginia as an example, the basic structure of thedirected graph used in the simulation is provided below. Similar to FIG.12, FIG. 38 also shows another example of an alcohol tree root of thedirected graph. It may begin with selecting a zip code. There are 47 zipcodes within Fairfax County, Va. The probability of selecting an agentfrom a zip code region can be made proportional to the population withinthe zip code. For example, zip codes with low availability of alcoholoutlets may be grouped together. Similarly, zip codes with mediumavailability of alcohol outlets may be grouped together. Likewise, zipcodes with high availability of alcohol outlets may be grouped together.

An agent can be selected within the zip code. The agent may be chosenbased on one or more factors, such as ethnicity and/or job class. Wheretwo or more factors are selected, they may be combined to form a jointdistribution. In this example, the joint distribution of ethnicity andjob class, as indicated in TABLE 1, was based on data from the U.S.Bureau of Labor Statistics (BLS) since data at the Fairfax County levelwas not currently available.

TABLE 1 Ethnicity v. Job Class Joint Probabilities Joint Probabilities(Ethnicity v. Job Class) White Collar Blue Collar Unemployed White 0.3370.613 0.052 Black 0.236 0.657 0.108 Hispanic 0.160 0.763 0.077

Another inquiry for the simulation can de determining whether the agentselected is a misuser of alcohol (also known as alcohol abuser) ornonmisuser (also known as non-alcohol abuser). “Misusers” are defined asindividuals who are either alcohol abusers or alcohol dependent asdefined in the NLAES data. In this example, the conditional probabilityof being a misuser may be dependent on ethnicity, job class and zipcode. The NLAES study generally provides that the conditionalprobability of being an alcohol misuser is conditioned on job class. BLSprovides the joint distribution of ethnicity and job class. Finally,Census provides data on ethnicity by zip code. To calculate theconditional probability of being a misuser given the ethnicity, jobclass and zip code, an assumption of conditional independence can bemade among these three probabilities. Based on this assumption, thedesired conditional probability can be approximated. These results arereflected in FIGS. 39-43.

Although these probabilities depend on ethnicity, job class, and zipcode, they do not take into account the availability within the zipcode. To approximate the availability effect, one or more assumptionscan be made. Within a given zip code, let n_(m) be the number ofmisusers, n_(n) be the number of nonmisusers, n_(p) be the population ofthe zip code, and n_(o) be the number of outlets. In one assumption,n_(o)≦0.5n_(p). In another assumption, n_(m) is proportional to n_(o).If n_(o)=0.5n_(p), then n_(m)=n_(p). If n_(o)=0, then n_(m)=0. Theseassumptions result in n_(m)=2n_(o). Therefore, discounting theethnicity, job class, and zip code factors, the n_(n)=n_(p)−n_(m).

Let P(m\e, j, z) be the probability of being a misuser given ethnicity,job class, and zip code and P(m\e, j, z, a) be the probability of beinga misuser given ethnicity, job class, zip code, and alcoholavailability. The excess probability due to availability can becalculated asP(m\e,j,z,a)=P(m\e,j,z)(1+2n _(o) n _(p)).  (1)

As equation (1) serves as a working approximation, it likely needs to becalibrated with real data. The maximum value of this factor is 2. Thus,it is possible to double the conditional probability of being a misuser,depending on the availability.

It should be noted that in this example, the probability of an acuteoutcome depends only on whether the agent is an alcohol misuser or not.However, such general assumption tends not to be realistic because oncesomeone is under the influence of alcohol, it generally does not matterwhat is the user's ethnicity, job class and/or home location. Thus,other factors (e.g., gender, age, etc.) need to be considered. Aspreviously mentioned, the present invention is flexible to be modifiedand allow more factors to be taken into account.

B. Hierarchical Statistical Estimation

A key element for estimating probabilities is to divide the populationinto relatively homogeneous subpopulations based on the idea that theirbehaviors with respect to alcohol will likely be relatively homogeneous.Even so, it is desirable to model variability into such a subpopulation.

Let D be the digraph representing the possible states of an individual,for example, traveling to work or in a bar or at home, working orengaged in recreation, sober or inebriated. As previously discussed,each simulated agent x moves randomly among the vertices of D accordingto agent-dependent, time-varying transition probabilities:P _(mn)(x;t)=Prob{Next node is n|Current node is m(and all other pastinformation)}  (2).

Ideally, a full model of the behavior of every individual should bemade. However, such a modeling effort generally entails estimation offar too many parameters relative to available data. Yet, at the sametime, variability among agents remains essential. As a compromise, it isenvisioned that Bayesian hierarchical models may be employed. Amanageably small number of classes of individuals representing differentsocio-demographic and geographical characteristics may be defined. Forexample, single college students living in Kansas City, Mo. may beselected as a class. Individuals in class X are to have random varying,but statistically, identical transition matrices. In essence, one wouldlikely need only to estimate a prior distribution on transition matricesassociated with each class.

Ignoring time dependence for the moment, the probabilities P_(mn) (x)are selected randomly from a distribution over transition matrices. Forboth modeling flexibility and technical reasons, the model may employclass-dependent Dirichlet prior distributions for each row of thetransition matrix. That is, with xε X and m fixed, the probabilitiesP(•)=P_(m) (x) may be sampled from a Dirichlet distribution:

$\begin{matrix}{{f\left( {p,\beta^{X,m}} \right)} = {\frac{1}{\psi\left( \beta^{X,m} \right)}{\prod\limits_{n}^{\;}\; P_{n}^{u_{n}^{X,m} - 1}}}} & (3)\end{matrix}$where ψ(β^(X,m))=Π_(n)Φ(β_(n) ^(X,m))/Φ(Σ_(n)β_(n) ^(X,m)) and β^(X,m)are class-dependent parameters to be estimated. Mathematically, theDirichlet distribution is the conjugate prior for the multinomialprobabilities represented by P_(m•)(x).

Alternatively, an assumption can be made where all individuals in aclass have exactly the same transition probabilities:P_(m•)(x)≡P_(m•)(X) for all xεX. This alternative is not likely toreduce the number of parameters to be estimated, but shouldsignificantly reduce the richness of the simulation. Time dependence canbe handled similarly. Time can be quantized, for example, into {morning,noon-time, early afternoon, late afternoon, evening, night}, which maylead to Dirichlet hyperparameters β^(X,m,τ), where τ is a time interval.In effect, time can be inserted into the modeling hierarchy.

In principle, it may also be necessary to model the sojourn times thatan agent spends in various vertices of the digraph. Markov renewalprocesses, which may allow such distributions to depend (sometimes only)on the current and next states, are a potential tool. However,estimation of class-dependent and time-dependent (hyper- or not) sojourndistributions tends to be impossible from currently available data.Minimally, a Markov assumption should be imposed that sojourn times, bedistributed exponentially and depend only on the current state (whichmay allow the use of hierarchical models for sojourn times as well asstates).

1. Estimation

The hierarchical model for transition probabilities contained inequations (2) and (3) requires estimation ofK _(X) ×|L| ² ×K _(τ)  (4)Dirichlet hyperparameters, where K_(x) is the number of classes, |L|² isthe number of vertices in the digraph and K_(τ) is the number ofintervals into which time is quantized. Plausible values for these areK_(x)=10, L=20 and K_(τ)=6, which leads to 24,000 parameters.

Although estimation of individual Dirichlet hyperparameters β^(X,m,τ) isstraightforward (relevant count data may be used directly), theestimation of these parameters from diverse and limited data tends to bea significant challenge. However, strategies remain available. Examplesof strategies include, but are not limited to, additional assumptions,structural assumptions (such as that on the digraph) and expert opinion.An example of an additional assumption is one that, for some verticesmβ^(X,m,τ), does not depend on X or τ. A structural assumption on thedigraph may force some of the P_(mn)(X,τ) to be zero, (i.e., some oreven many transitions may be impossible). Expert opinion may be used toprovide values of β^(X,m,τ) for which data are nonexistent or too weak.

An extreme, but potentially viable assumption for sojourn timedistributions may be that the distribution of the sojourn time in vertexm is an exponential distribution depending only on m, and not on theagent's class or the time of day. In such case, only as many exponentialparameters (of associated Gamma distribution hyperparameters) may needto be estimated. Yet, a significant problem may still remain:characterization and quantification of the uncertainties associated withvarious estimated values and propagation of these to uncertainties inthe output of the simulation.

2. Simulation

In part to characterize uncertainties, and in part, because closed formcomputation of probabilities of adverse outcomes is not feasible, it isnecessary to perform at least one simulation (sometimes multiplesimulations). Principal steps in this process include the following.One, for each individual agent x, determine its class X(x). Thisdetermination may be done either deterministically or stochastically.Two, for each agent in class X and each time interval τ, stochasticallygenerate transition probabilities P_(mn)(X,τ) using equation (3)Dirichlet distributions with hyperparameters β^(X(x),•,τ). It can be thecase that a more efficient implementation can generate β^(X(x),m,τ) onlyif x ever enters vertex m during time interval τ. Three, stochasticallygenerate sojourn time distributions D_(mn)(x,τ). Four, using thestochastically generated transition probabilities P_(mn)(x,τ) andsojourn time distributions D_(mn)(x,τ), simulated and record theday-long path of agent x through the digraph.

The computational effort should be, to first approximation, linear inthe number of agents and be quadratic in the number of vertices in thedigraph. This effort is feasible. Also, this effort should be linear inthe number of replications of the simulation.

3. Possible Simplifications

A strategy for modeling transition probabilities is to augment equation(3) with dependence on covariates. This kind of modeling can be usefulin modeling the dependence of transitions on covariates, such as age,gender, time of day, etc. Several kinds of discrete choice models may bepossible. For simplicity, one may consider the multinomial logisticmodel for k choices specified as follows. For agent x, suppose there arecovariate vectors v_(xm1), . . . , v_(xmk) and a parameter vector w suchthat the probability of making choice n, 1≦n≦k, given state m is:

$\begin{matrix}{{P_{mn}(x)} = {\frac{{\mathbb{e}}^{\varphi_{xmn}^{\prime}w}}{\sum\limits_{i = 1}^{k}{{\mathbb{e}}\;\varphi_{xmi}^{\prime}w}}.}} & (5)\end{matrix}$

As a simple example, consider the following model. Suppose an agent hasfinished work and must make a choice among three activities: (1) gohome, (2) stop for a drink or (3) do some other leisure activity, suchas eating or shopping away from home. The probability model may dependon two or more other factors, such as the agent's gender and the time ofday. For simplicity, time of day may be discretized to six periods.Then, a simple model can be coded as:φ′_(xmi) w=x _(1i) w ₁ +x _(2i) w ₂ +x _(3i) w ₃ +l _(x) w ₄ +d _(x1) w₅ +d _(x2) w ₆ +d _(x3) w ₇  (6)where the x_(ni) terms are dummy variables for activities,x_(ni)=1, n=i and x_(ni)=0, n≠i.

Similarly, l_(x)=1 if agent x is male and zero otherwise is a dummyvariable for gender. The t_(xn) are dummy variables for three of thefour time-of-day periods.

More generally, this discrete choice model can be used as part ofahyperprior specification for the Dirichlet prior of equation (3). Underequation (3), the prior mean for choice n is:

$\begin{matrix}{{E\text{(}{P_{mn}(x)}\left. \beta^{X,m} \right)} = {\frac{{\mathbb{e}}^{\beta_{n}^{X,m}}}{\sum\limits_{i}{\mathbb{e}}^{\beta_{i}^{X,m}}}.}} & (7)\end{matrix}$

Therefore, the parameters of the Dirichlet prior for agent x can bemodeled as:β_(n) ^(X,m)=φ′_(xmn) w+ε _(xn)  (8)for suitable independent mean zero random variables ε_(xn) and agent xin class X. The number of transition probabilities to be estimated byjudicious use of the model.

Estimates of a portion of these probabilities can be obtained from tripand activity data sets. For instance, the choice model without thedrinking choice may be estimated from such a data set. One property ofthe simple multinomial logistic choice model is independence ofirrelevant alternatives. Specifically, the following simplified choicemodel can be considered:

$\begin{matrix}{{P_{mn}(x)} = {\frac{{\mathbb{e}}^{\beta_{n}}}{\sum\limits_{i = 1}^{k}{\mathbb{e}}^{\beta_{i}}}.}} & (8)\end{matrix}$

For any subset N⊂{1, . . . , k}, it can be easy to show that theconditional probability of choice n given nε N is simply

$\begin{matrix}{{P_{mn}\text{(}x\left. {n \in N} \right)} = {\frac{{\mathbb{e}}^{\beta_{n}}}{\sum\limits_{i \in N}{\mathbb{e}}^{\beta_{i}}}.}} & (9)\end{matrix}$

These calculations mean that certain parameters can be estimated frommarginal data. One can estimate some parameters in these models fromother data sets (such as a Portland, Oreg. data set) without drinkingbehavior. Furthermore, one can add parameters for drinking alternativesto match conditional probabilities from other surveys.

VI. ALCOHOL TREE SIMULATOR

A. Terminology

HTML is the coding language used to create Hypertext documents for useon the World Wide Web. HTML is a standard of the World Wide WebConsortium (W3C).

DHTML, or Dynamic HTML, is a method of combining HTML, CSS, DOM, andscripting languages (such as Javascript, ECMAScript, etc.) to allow fordynamic client-side manipulation of presentational components. When usedappropriately, DHTML can eliminate the need for a server request eachtime an action is to be performed, dramatically increasing the speed ofinteraction with the application.

CSS, or Cascading Style Sheets, is a specification for the presentationof HTML marked documents. CSS works like a template, allowing Webdevelopers to define styles for individual HTML page elements. CSS is astandard of the World Wide Web Consortium (W3C).

DOM, or the Document Object Model, is a programming interface thatallows HTML pages and XML documents to be created and modified as ifthey were program objects. DOM makes the elements of these documentsavailable to a program as data structures, and supplies methods that maybe invoked to perform common operations upon the document's structureand data. DOM is both platform-neutral and language-neutral. It is alsoa standard of W3C.

Javascript (formally known as ECMAScript, as defined by the ECMA-262standard) is a scripting language originally developed by Netscape. Itis commonly used to make HTML documents more interactive, as it allowsdirect access to the underlying page DOM. Despite its name, JavaScriptis not related to Java.

ASP.NET (sometimes referred to as ASP+) is the latest version ofMicrosoft's Active Server Pages technology (ASP). ASP.NET is drasticallydifferent than its predecessor in three major ways. One, it supportscode written in compiled languages such as C++, C#, and J#. Two, itfeatures server controls that can separate code from the content,allowing WYSIWYG editing of pages (when using the Visual Studio .NETInteractive Development Environment (IDE)). Three, it is fullyObject-Oriented; based on the .NET runtime, it has full access to theunderlying .NET class library.

Although ASP.NET is not backwards compatible with ASP, it is able to runside by side with ASP applications.

DLL, or Dynamic Link Library, is a file of functions that is compiled,linked and saved separately from the processes that use them. DLLfunctions can be used by more than one running process. The operatingsystem maps DLLs into the process's address space when the process iseither starting up or while running.

WYSIWYG is an acronym for “what you see is what you get.” WYSIWYG HTMLEditors like Dreamweaver or Frontpage let one create web pages bydisplaying exactly how it will look in a browser. With this feature,intrinsic knowledge of HTML is not necessary. However, using WYSIWYGeditors tends to be problematic because of their use of non-standard,proprietary and deprecated mark-up. Therefore, the present inventiondoes not use any WYSIWYG editors.

Java, developed by Sun Microsystems, is a network-oriented programminglanguage that is specifically designed for writing programs that can besafely downloaded to the computer through the Internet and immediatelyrun without fear of viruses or other harm to the computer or files.Using small Java programs (called Applets), Web pages can includefunctions such as animations, calculators and other fancy tricks. Javais a simple, robust, object-oriented, platform-independent,multi-threaded, and dynamic general-purpose programming environment. Itis best for creating applets and applications for the Internet,intranets and any other complex, distributed network.

J#.NET is a powerful tool for Java-language developers who want to buildapplications and services on the Microsoft NET Framework. It targets the.NET Framework version 1.1, is fully integrated with Visual Studio .NET,and provides added support for building Mobile Web applications. J#.NETincludes technology that enables users to migrate Java-language programsto the .NET Framework (often with minimum time). Existing applicationsdeveloped with Java can be easily modified to execute on the .NETFramework, interoperate with other Microsoft .NET connected languagesand applications, and incorporate .NET functionality, such as ASP.NET,ADO.NET and Windows Forms. It should be noted that J#.NET is not a toolfor developing applications intended to run on a Java virtual machine.Applications and services built and compiled as J# (.NET) code generallyruns only in the .NET Framework; they typically do not run on any Javavirtual machine. Independently developed by Microsoft, J#.NET is neitherofficially endorsed nor approved by Sun Microsystems, Inc.

Just-In-Time or JIT refers to a compiler for the Java language thatallows interpreted Java programs to be automatically compiled intonative machine language on the fly, for faster performance of theprogram.

B. Component Overview

Components of the simulator can be written using Java and J#.NET. Bothare object-oriented development languages implementing the Sun JavaLanguage Specification. These two languages are not binary compatible.However they are mostly source-compatible.

Both these compiled languages are similar to C and C++. A majordifference is in the “level of compilation” achieved. In C, forinstance, the code would be compiled down to raw x86 (assembly language)instructions, which would form the binary executable. One problem withthis method of compilation is portability. If code is run on a differentplatform or architecture, many system calls and hooks using #IFDEFpreprocessor logic must generally be changed. Basically, the compileritself is allowed to conditionally include portions of code depending onarchitecture type. For instance, to target a WIN32 architecture, thecode is placed inside an #IFDEF WIN32 block, in which case it would onlybe compiled if the architecture matched. Such placement is standard Cand C++ fare. In Java, however, since the binary executable containsVirtual Machine (VM) code, which is one level above Assembly Language,the VM can execute the code in the same fashion regardless of whichsystem architecture is currently being used. One can think of this, in asense, as a form of integrated compatibility layer built into thelanguage itself. However, VM contains a very sophisticated method ofdynamically compiling code JIT as the executable is running. Anadditional benefit of this JIT Compilation is that it can performarchitecture-specific optimizations at runtime, something a traditionalC or C++ program can likely never (let alone reasonably) achieve. Theseoptimizations can dramatically increase the speed of the executing Javaapplication, sometimes making them even faster than their C and C++counterparts.

The present invention can be hosted on an IIS6.0 web server runningASP.NET with the NET Framework version 1.1.

C. Simulator Processes

The alcohol tree simulator may be a web-based application or a softwareprogram. It may even be built into a device. The alcohol tree simulatormay incorporate three main processes of functionality: the alcohol treesimulation process, the map generation process and the presentation and(client-side) user-interaction process.

1. Alcohol Tree Simulation Process

The alcohol tree simulation process is the one that actually performsthe simulation given the number of agents and runs. This program maycomprise a multitude of Java classes. Each Java class may be containedin a file of the same name. For instance, a class named “Node” will bein a file named “Node.java.” The program may be designed using a “tree.”

Trees in computing are similar to real-life trees. A computing tree hasa “root” node along with one or more hierarchal child nodes. Therootnode is the node at the top of the tree, with no parent in thehierarchy.

The “AlcoholTree” class can serve as the main class for the program. Itcan perform the actual simulation given the number of agents and outputfile it should use. Typically, there is a command line version of thesimulation, such as java AlcoholTree 1000000 C:\fairfax.txt. Thiscommand line may be found in the same command line directory as thecompiled java code (.class files).

The “Node” class is the base class for the program. It generallydescribes a default node that is capable of having multiple children,which may be defined by each individual class. The “OutletNode” classextends the “Node” class; in essence, it intrinsically inherits theattributes of the “Node” class. Each of the other classes, in turn, mayinherit from another class. FIG. 44 illustrates an example of the orderof inheritance. In particular, JAVA class nodes and their relationshipare shown.

In reality, there is often no reason for each of the different nodeclasses to inherit from the previous node class. They could all just aseasily inherit from the base “Node” class itself. The reason for havingsuch a structure is if the subsequent child node classes require accessto variables or functions in the parent node class. However, such a casedoes not really apply here.

As previously noted, FIG. 12 shows an example of an abbreviated alcoholtree. Because of size constraints, this tree does not include everypossible combination. Each level of the tree may be repeated for eachnode above it. For instance, the “Non Alcohol Misuser” node here alsohas a “Simulated Day” child node with corresponding seven acuteoutcomes. Similarly, there are “Alcohol Misuser” and “Non AlcoholMisuser” child nodes for the “White Collar” and “Unemployed” nodes. Froma literal interpretation, it may appear that only the “Blue Collar” nodehas “Alcohol Misuser” and “Non Alcohol Misuser” nodes, which is not thecase.

Each of the Node classes contains probability values that may beutilized by the simulation in determining whether an acute outcomeoccurs. The probabilities may correspond to the different types of thenode. For example, there can be three types of OutletNodes—one for eachlow alcohol availability, medium alcohol availability and high alcoholavailability.

The alcohol tree simulation process may begin by inputting a number ofagents. For a particular geographic area (e.g., town/city, county,state, nation, etc.) of interest, a default number may be entered. Thisnumber may be based upon Census data.

2. Map Generation Process

Results from the alcohol tree simulation process may be generated as amap for display. Initially, processing actual GIS shapefile data mayneed to take place by processing both the .SHP file and the .DBF file.The .SHP file contains zip code regional point data as pairedlatitudinal and longitudinal coordinates. The .DBF file associates anactual zip code number with the grouped coordinate data previously readfrom the .SHP file. This .DBF file may also contain additionalinformation, such as the computed regional area.

The .SHP, or ESRI Shapefile, data format stores non-topological geometryand attribute information for the spatial features in a data set in abinary file. The geometry for a feature is stored as a polygonal “shape”comprised of a set of vector coordinates (representing latitude andlongitude).

An ESRI “shapefile” comprises a main file and a dBASE table (of the samename, with .DBF extension). The main file is of variable-record-length;each individual record describing a shape with a list of its vertices.The dBASE table contains feature attributes with one record per feature,causing a one-to-one relationship between the geometrical and attributedata between files. For reading in the ESRI shapefiles, slightlymodified classes (or components) originally from the CCmap applicationmay be used. Essentially, this modification may allow for the openingand reading in the raw binary data from the underlying file “stream,”according to the ESRI data specification. These data may be storedlocally for later use.

Next, the coordinate data may be scaled from latitudinal andlongitudinal coordinates to X and Y pixel values. A simple operation maybe used to accomplish this task, where x=original X value (longitude),bL=minimum X value in the entire GIS shapefile (bounds), bH=maximum Xvalue on the entire GIS shapefile (bounds) and d=resulting imagedimensions (width and height). Scaled X can then be seen as:

$\begin{matrix}{{{Scaled}\mspace{14mu} X} = {\frac{\left( {x - {bL}} \right) \times d}{{bH} - {bL}}.}} & (10)\end{matrix}$Likewise, scaled Y can be seen as:

$\begin{matrix}{{{Scaled}\mspace{14mu} Y} = {\frac{\left( {y - {bL}} \right) \times d}{{bH} - {bL}}.}} & (11)\end{matrix}$

With their scaled X and Y pixel positions, a list of zip code regionscan be drawn. Each of these regions can be logically organized as a“ZipcodeRegion” object.

Afterwards, the maximum number of acute outcomes in a single zip coderegion can be calculated. Where maps are to be presented in color, thismaximum number can be used later to dynamically adjust color intensity.

It should be noted that while the maps may be presented in color, thepresent invention also allows for drawings to be displayed in black andwhite. For example, FIG. 45 shows the intensity and representative scaleof acute outcomes with probabilities based on actual data. Hence, colormap characterization represents just one aspect of practicing thepresent invention and is not to be construed as the only way ofrepresenting data results.

Zip code regions may be drawn as closed polygons using their scaled Xand Y values. Then, the zip code region intensity may be calculated todetermine the color used to fill the particular region. The intensitycan be calculated as the percent of acute outcomes occurring in anindividual region relative to the maximum number of acute outcomeshappening in any region (calculated previously). Based on this“intensity value,” the actual color value (amount of red, green, andblue) is scaled accordingly.

A computer screen comprises of approximately one million pixels. Each ofthese pixels has a red, green, and blue component. A value of 0 (nocolor) to 255 (full color) is assigned to each of these components todetermine its color. White can be created by combining all of thecolors; in essence, white can be represented as {R=255, G=255, B=255}.Shading can also be varied. For example, to vary the shading betweenwhite and red, the green and blue values can be modified while keepingred constant at 255 based on the intensity value previously calculated.The default value for each polygon is typically white (where R, G,B=255). B and G values may be decreased inversely proportional to theratio of the outcomes in one area to the maximum value in any area.Where the intensity value is used to directly determine the amount ofgreen and blue of the individual pixel, a maximum of 254 shades betweenred and white can be obtained. In this case, a solid red represents aregion with the highest number of acute outcomes, whereas a solid whiteregion represents a region with the lowest number of acute outcomes.

Pie charts may be used to represent populations, such as Blacks, Whitesand Hispanics, within each individual zip code region. These are simplepercentage calculations using population data, which are used todetermine circular angles for each of the three chunks of the pie chart.The charts can be also drawn at region centroids. Exemplified colorrepresentation for the pie slices may be as follows: black for Blacks,white for Whites and tan for Hispanics.

Generated maps may also reflect alcohol sellers, alcohol establishments,names of regions, population graphs, road names, etc. Alcohol sellersand alcohol establishments may be located on the maps according to theirlatitude and longitude coordinates. Each of these data may, singularlyor in combination with other data, overlap the intensity of acuteoutcomes.

3. Presentation and (Client-Side) User-Interaction Process

In general, the presentation layers are two-fold: the server back-endcomponent, which can be an ASP.NET script, and the client, which can bethe users' web browser.

As HTTP by nature is a stateless protocol, internal sessions (viacookies) may be used to maintain persistence across web-requests. Bydefault, a single web page “hit” will likely generate two actualunderlying requests. The first may output the Alcohol Tree Simulatorinterface, with its buttons and fields. The second may retrieve theactual map image (previously created) to be displayed.

This process may begin with using the library from the Alcohol TreeSimulation Process to perform the actual simulation, given the number ofagents to be simulated. This simulation is likely to occur only on thefirst of these two underlying requests, as it may be erroneous tore-simulate before the map image is generated and shown.

At this point, an interface may be presented to the user. As oneembodiment, the interface may allow client-side interaction via, forexample, “DHTML,” or Javascript. In other words, the user has theability to modify data, such as region centroid, region name, alcoholestablishment, and alcohol seller (such as the Alcohol Beverage Control(ABC) stores of Virginia). Additionally, where demographic and zip codeinformation are displayed, the proportion of each race and the number ofalcohol establishments may be displayed. Such proportion may be adjustedwithin, for example, the zip code to see zip code specific effectsrather than county-wide effects.

The user may also be presented with the underlying probability valuesused to generate the simulation results. Furthermore, the user has theability to change and re-run the simulation with new values.

As another embodiment, the interface may allow the user to modify theprobabilities used during the simulation. For instance, if an ethnicityadjustment is made in this mode, the individual zip code data can besuppressed and population proportions can be taken county-wide. However,using this mode, the probabilities associated with alcohol availabilitywithin zip codes may not be modified. Likewise, alcohol availability canalso be similarly adjusted. However, using this latter mode, theprobabilities associated with ethnicity within zip codes may not bemodified. The simulation may be rerun and the corresponding map can beredrawn accordingly to reflect its new outcome status. Widgets may beused to assist the user to make modifications. For example, FIG. 46shows a rerun simulation with modifications showing only low outletavailability in each zip code.

Data from the alcohol establishment and alcohol seller may be outputtedby the ASP.NET script as HTML elements. These elements may be bydefault, be hidden and be seen as small, square, colored boxes. Theseboxes may be shown and hidden via DHTML and underlying DOM elementaccess. More specifically, JavaScript can be used to access the DOM,through which the Document's CSS elements can be accessed and modifiedaccordingly (i.e., to being hidden or visible).

To retrieve the latitudinal and longitudinal coordinates of the alcoholsellers and alcohol establishments, a separate tool may be created toautomate the otherwise quite lengthy and error-prone process. This toolcan be used to read in individual establishment locations from an XLSspreadsheet file and pass address information onto map query (such asGoogle Maps). The resulting HTML page may then be analyzed and parsed todetermine the actual latitude and longitude of the specified address. Itmay be the case that the coordinates may be hidden in the map queryinterface in a custom HTML tag, which may define these values as xPosand yPos attributes. The tool may then store these latitudinal andlongitudinal values to a simple text file (one entry per line) to belater read and processed as necessary. An advantage of using this toolis to serve as a look-up table to facilitate address lookups.

The map presented to the user can be made interactive through the use ofan HTML “imagemap,” which causes an inline frame to displayregion-specific demographic information (static HTML files,auto-generated by a web-extractor tool for this purpose).

The user may be presented with an option of displaying SimulationStatistics or Detailed Simulation Output. Simulation Statistic(percentage) values may be calculated relative to the total number ofagents.

As for the Detailed Simulation Output, a multitude of tables may begenerated and presented to the user. FIGS. 47-50 exemplify a detailedoutput of the level of alcohol availabilities. One table may show thenumber of acute and benign outcomes for all alcohol misusers andnonmisusers. Another table may show the number of acute and benignoutcomes for all White, Black and Hispanic individuals. Yet, anothertable may show the number of acute and benign outcomes for all WhiteCollar, Blue Collar and Unemployed individuals. A further table mayrelate actual local police record data to the simulation results byoutcome type and may also calculate the standard deviation. The actualpolice record data can be for a certain time period (such as five-yearperiod) so that the data may be rescaled to a one-year period. Thesimulation results may also be scaled accordingly if one million agentswere not used so that the standard deviation calculations areappropriate. Where the locality to be simulated has a population ofapproximately one million, the a nominal standard may be set to onemillion agents. Although the simulation can be done with virtually anynumber of agents, scaling may be adjusted to make simulated statisticsconsistent.

a. Alcohol Tree Modified Probability Utilization

A map key can be generated using the maximum number of acute outcomesfor a given region, which is calculable in the Map Generation Process,and dividing that number into a number (such as 12) of color shades ofvarying intensity. An example of color shading intensity is betweensolid red and solid white. Each of the color values should have an upperand lower bound to it, which may result in the key appearing “seamless.”The upper bound color can be the one representing the high-end number ofacute outcomes represented by the color shade. Likewise, the lower boundcan be the one representing the low-end number of acute outcomesrepresented by the color shade. Therefore, if a key item had theboundaries of, for instance, 0 to 50, and was colored from white at thebottom to slight pink on the top of the item boundary, then a value of25 should be located between these two points in color.

b. Color Coded Establishment Types

To color code establishment types, the previously described tool may beused to automate the retrieval of latitudinal and longitudinalcoordinates from the map query. Data may be stored in the initial textfile, which may contain addresses, zip codes, latitudinal/longitudinalcoordinates, etc.

The tool can be modified to include alcohol license types in theresulting text file. These types may represent various licenses asinteger values internally. For example, where license information cannotbe determined, then integer value zero may be designated. If theestablishment has a license to serve alcohol on premise, then integervalue one may be designated. If the establishment has a license to servealcohol off premise, then integer value two may be designated. If theestablishment has a license to serve alcohol both on and off premise,then integer value three may be designated.

In the ASP.NET script, after reading in the latitudinal and longitudinalcoordinate data, coloring for the establishment point may be determinedbased on the license value integer previously described. If theestablishment has a value of two (off premise only) then it is colored adifferent color, such as blue. If the establishment has a value of oneor three, then it may be colored another different color, such as green.

4. Additional Data

It should be noted that there may likely be occurrences in the generatedmap where a single zip code has multiple physical boundary regions forit. The reason for this effect is that in the underlying GIS data usedto generate the map, there may be times when a single zip code containsmultiple physically decoupled “polygonal regions,” which in some casesare located within a different zip code region entirely. As an aspect ofthe present invention, the GIS data being used is the “5-digit Zip CodeBoundary” Census data.

It should be further noted that zip codes merely present one embodimentof how the present invention can be used. One skilled in the art wouldrecognize that other forms of boundaries (such as those for determiningand selecting agents and/or actors, geographical boundaries, etc.) canbe used. Nonlimiting examples include Census tracks, Census blocks,police patrolling areas, zoning districts, political districts, schoolzones, residential v. commercial areas, etc.

The detailed simulation output may be written for a detailedrepresentation of the simulated “alcohol tree,” displayed in itshierarchical form as child “nodes” of one another. At the roothierarchy, three alcohol availability outlets (namely low, medium andhigh) may be seen. Within each of these outlets are race classes, suchas Black, White and Hispanic. Each of these race classes can containthree job classes, namely white collar, blue collar and unemployed.Underneath the job classes can lie a node representing alcohol usage,such as either misuser or non misuser. Within each of these individualnodes may lie final outcome nodes. These may include DWI, assault,murder, sexual assault, domestic violence, child abuse, suicide, and afinal node to represent all simulated benign outcomes.

Table 2 Shows Mean Simulated Results for Fairfax County, Va. Model.

TABLE 2 Mean Simulated Results for the Fairfax County, VA Model FairfaxCounty, VA Model Average RFA 0.001365 Average RFB 0.948489 AverageNumber of Acute Outcomes 1365 Average Number of Benign Outcomes 948489Average Number of Agents 949855 Average Number of Runs 1000000 AverageTotal DWI 707.5 Average Total Assault 132.2 Average Total Murder 7.4Average Total Sexual Assault 33.9 Average Total Domestic Violence 168.2Average Total Child Abuse 216.4 Average Total Suicide 99.8

“Actual Incidents” is calculated as the total number of each incidenttype from the police data file divided by the number of years of datacontained in the file.

Simulated Results are calculated as the total number of each individualoutcome type multiplied by a “scaling” factor. Each individual outcometype can be obtained by iterating over all alcohol tree node elementsand retrieving ones which apply. In this case, only outcome type nodesappear to apply.

The “scaling” factor may be applied if the simulation is not run withone million agents. By scaling the total number of acute outcomesaccording to the number of agents the simulation may be run, based onthe percentage of agents out of one million agents, for the data betweencolumns. The columns, both actual and simulated, may be comparable.

“Mean Simulated Results” may be calculated by scaling the averaged dataaccordingly (such as multiplying by ten) to more accurately representthe actual number of “agents” (such as 1,000,000) desired.

The values shown in TABLE 2 appear very close to the actual datacollected from the police file. TABLE 3 shows a comparison of simulateddata, rounded to whole numbers, and actual acute outcomes.

TABLE 3 Comparison of Simulated with Actual Acute Outcomes for theFairfax County, VA Model Fairfax County, VA Model Actual [Historical]Incidents Mean (1 Year) - Simulated Simulated Mean Outcome Data fromResults Results Square Absolute Type Police File (1 Year) (1 Year) ErrorDeviation DWI 722 658 708 19.6 6.4 Assault and 133 107 132 2.0 2.6Battery Murder 6 4 7 1.0 2.0 Sexual 32 38 34 4.1 6.2 Assault *Domestic41 168 168 16.1 12.7 Violence Child Abuse/ 84 213 216 17.4 12.9 NeglectSuicide 49 84 100 2.6 2.2 Benign 998933 998728 998635 888.4 7.5 *Inaddition to the Domestic Violence count, there is an actual DomesticDispute count of approximately 6720 disputes per year.

Based on 100 Monte Carlo replications, the Mean Square Error (MSE) canbe calculated using the following formula:

$\begin{matrix}{{MSE} = {\frac{1}{100}{\sum\limits_{i = 1}^{100}\left( {{simulated}_{i} - {actual}} \right)^{2}}}} & (12)\end{matrix}$where i is the varying acute outcome (i.e., DWI, assault and battery,murder, sexual assault, domestic violence, child abuse, suicide, andbenign outcomes). Absolute Deviation may be calculated as follows:

$\begin{matrix}{{AbsoluteDeviation} = {\frac{1}{100}{\sum\limits_{i = 1}^{100}{{{{simulated}_{i} - {actual}}}.}}}} & (13)\end{matrix}$

VII. GEOSPATIAL VISUALIZATION OF ACUTE OUTCOMES

Given the alcohol tree structure illustrated in FIG. 12 and theconditional probabilities developed from collected data, a visualizationof the geospatial location of acute outcomes within Fairfax County, Va.may be constructed. The data can be aggregated at various levels. Forexample, the data may be aggregated spatially to the 47 postal codes inFairfax County. FIG. 45 illustrates the distribution of acute outcomeswithin Fairfax County. FIG. 51 displays alcohol sellers (VA ABC stores)within Fairfax County. FIG. 52 shows alcohol establishments (bothoff-premise and on-premise) that are licensed to sell alcohol.

The distribution of acute outcomes in FIG. 45 represents results basedon actual data. Higher levels of acute outcomes are indicated by denserdots. In FIG. 52, black squares indicate the location of on-premiseoutlets, such as bars, taverns, restaurants, etc. Meanwhile, whitesquares indicate the location of off-premise outlets, such as grocerystores, convenience stores, etc. In FIG. 51, the black squares indicatethe location of state-owned distilled spirits outlets. Fairfax Countyhas approximately 866,000 individuals who are either White, Black orHispanic. The alcohol tree simulator simulated the approximately 866,000individuals. FIGS. 45, 51 and 52 are based on a simulation using actualconditional probabilities derived from the data and is well calibratedto the actual outcomes experienced during the years 2002 and 2003 fromwhich the data were collected. In FIG. 45, the denser dots on theright-hand side of the map correspond to the City of Alexandria.

A purpose of this tool is to not only see the current geospatialdistribution of acute outcomes but to also see what could happen ifparameters of the distribution are adjusted, e.g. fewer alcohol outlets,more policing, racial or population shift, etc. FIGS. 45 and 46 areillustrations of existing alcohol-related acute outcomes and what couldhappen with population shifts.

In FIG. 46, the area with denser dots at the bottom of the page is FortBelvoir, a U.S. Army base. The area with denser dots near the top is thetown of Herndon. The number of soldiers based at Fort Belvoir has beenincreasing dramatically. Likewise, the Hispanic population in Herndonhas also been increasing dramatically. This figure illustrates whatcould happen if there are substantial population shifts. Here, thedensity of dots corresponds to the most acute outcomes. The lack ofdenser dots in Alexandria does not mean that the number of acuteoutcomes have decreased there. Rather, it demonstrates that the numbershave increased elsewhere. In essence, the manner of interventions can beexplored with resulting geospatial illustrations of their impact.

VIII. CONCLUSION

The stochastic digraph model provides an effective tool for simulatingthe acute violence-related effects of alcohol misuse. In addition, itprovides a tool for exploring the consequences of various interventionsby adjusting conditional probabilities. Geospatial visualization aspectscan allow policy makers to explore “hotspots” that may be potentiallocations for additional interventions.

The foregoing descriptions of the embodiments of the claimed inventionhave been presented for purposes of illustration and description. Theyare not intended to be exhaustive or be limiting to the precise formsdisclosed, and obviously many modifications and variations are possiblein light of the above teaching. The illustrated embodiments were chosenand described in order to best explain the principles of the claimedinvention and its practical application to thereby enable others skilledin the art to best utilize it in various embodiments and with variousmodifications as are suited to the particular use contemplated withoutdeparting from the spirit and scope of the claimed invention. In fact,after reading the above description, it will be apparent to one skilledin the relevant art(s) how to implement the claimed invention inalternative embodiments. Thus, the claimed invention should not belimited by any of the above described example embodiments. For example,the present invention may be used to analyze and determine policyimplementations for drugs (e.g., illegal substances, over-the-counterdrugs, prescription drugs, etc.), tobacco, banks and similar financialinstitutions, gas stations, food vendors, retail stores, wholesalestores, sporting events, concerts, diseases and/or other medical-relatedissues, healthcare, homeland security, elections, etc.

In addition, it should be understood that any figures, graphs, tables,examples, etc., which highlight the functionality and advantages of theclaimed invention, are presented for example purposes only. Thearchitecture of the disclosed is sufficiently flexible and configurable,such that it may be utilized in ways other than that shown. For example,the steps listed in any flowchart may be reordered or only optionallyused in some embodiments.

Further, the purpose of the Abstract is to enable the U.S. Patent andTrademark Office and the public generally, and especially thescientists, engineers and practitioners in the art who are not familiarwith patent or legal terms or phraseology, to determine quickly from acursory inspection the nature and essence of the claimed invention ofthe application. The Abstract is not intended to be limiting as to thescope of the claimed invention in any way.

Furthermore, it is the applicants' intent that only claims that includethe express language “means for” or “step for” be interpreted under 35U.S.C. §112, paragraph 6. Claims that do not expressly include thephrase “means for” or “step for” are not to be interpreted under 35U.S.C. §112, paragraph 6.

A portion of the claimed invention of this patent document containsmaterial which is subject to copyright protection. The copyright ownerhas no objection to the facsimile reproduction by anyone of the patentdocument or the patent invention, as it appears in the Patent andTrademark Office patent file or records, but otherwise reserves allcopyright rights whatsoever.

1. A tangible computer-readable medium encoded with instructions forcreating a social network stochastic directed graph model based on astochastic directed graph, wherein execution of said “social networkstochastic directed graph model” by one or more processors causes said“one or more processors” to perform the steps comprising: a. using anactivity generator for creating agents in the stochastic directed graphto represent a population stratum; b. selecting said agentsproportionally to the size of said population stratum and representativeactivities associated with said population stratum, said agents having adynamically adjustable and time-varying conditional probability attachedto said activities indicating the likelihood of interaction between saidagents with at least one other agent or actor; c. accumulating outcomesfor said interaction, said outcomes including benign outcomes and acuteoutcomes; and d. estimating a multinomial probability distribution basedon said outcomes.
 2. The tangible computer-readable medium according toclaim 1, further including introducing a hierarchical Bayesian structureto said stochastic directed graph.
 3. The tangible computer-readablemedium according to claim 1, wherein vertices of said stochasticdirected graph represent the state of said agents.
 4. The tangiblecomputer-readable medium according to claim 1, wherein edges of saidstochastic directed graph represent at least one decision that takessaid agents from one state into another state.
 5. The tangiblecomputer-readable medium according to claim 4, wherein at least one ofsaid conditional probability is attached to said edges.
 6. The tangiblecomputer-readable medium according to claim 1, wherein each of saidagents is a member of a class, said class being homogenous.
 7. Thetangible computer-readable medium according to claim 1, furtherincluding developing interactions among said agents to adjust saidconditional probability that is in connection with said acute outcomes.8. The tangible computer-readable medium according to claim 1, whereinthe structure of said stochastic directed graph and said conditionalprobability is based on collected data, said collected data including atleast one of the following: a. surveyed data; b. census data; c.administrative record data; d. national data; e. state data; f. localdata; g. expert opinion; and h. any combination thereof.
 9. The tangiblecomputer-readable medium according to claim 1, wherein the stochasticdirected graph neither encodes conditional independence relationshipsnor infers causality.
 10. A social network stochastic directed graphsystem that uses at least one stochastic directed graph for formulatingpublic policy comprising: a. an activity generator configured forcreating agents in the stochastic directed graph to represent apopulation stratum; b. an agent selector configured for selecting saidagents proportionally to the size of said population stratum andrepresentative activities associated with said population stratum, saidagents having a dynamically adjustable and time-varying conditionalprobability attached to said activities, said conditional probabilityindicating the likelihood of interaction between said agents with atleast one other agent or actor; c. an outcome accumulator configured foraccumulating outcomes for said interaction, said outcomes includingbenign outcomes and acute outcomes; and d. an estimator configured forestimating a multinomial probability distribution based on saidoutcomes.
 11. The social network stochastic directed graph systemaccording to claim 10, wherein said stochastic directed graphincorporates a hierarchical Bayesian structure.
 12. The social networkstochastic directed graph system according to claim 10, wherein verticesof said stochastic directed graph represent the state of said agents.13. The social network stochastic directed graph system according toclaim 10, wherein edges of said stochastic directed graph represent atleast one decision that takes said agents from one state into anotherstate.
 14. The social network stochastic directed graph system accordingto claim 13, wherein at least one of said conditional probability isattached to said edges.
 15. The social network stochastic directed graphsystem according to claim 10, wherein each of said agents is a member ofa class, said class being homogenous.
 16. The social network stochasticdirected graph system according to claim 10, further including aninteraction developer, said interaction developer configured fordeveloping interactions among said agents to adjust said conditionalprobability that is in connection with said acute outcomes.
 17. Thesocial network stochastic directed graph system according to claim 10,wherein the structure of said stochastic directed graph and saidconditional probability is based on collected data, said collected dataincluding at least one of the following: a. surveyed data; b. censusdata; c. administrative record data; d. national data; e. state data; f.local data; g. expert opinion; and h. any combination thereof.
 18. Thesocial network stochastic directed graph system according to claim 10,wherein the stochastic directed graph neither encodes conditionalindependence relationships nor infers causality.